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The Lowest Radial Distance (LoRaD) method is a modification of the recently introduced Partition-Weighted Kernel method for estimating the marginal likelihood of a model, a quantity important for Bayesian model selection. For analyses involving a fixed tree topology, LoRaD improves upon the Steppingstone or Thermodynamic Integration (Path Sampling) approaches now in common use in phylogenetics because it requires sampling only from the posterior distribution, avoiding the need to sample from a series of ad hoc power posterior distributions, and yet is more accurate than other fast methods such as the Generalized Harmonic Mean (GHM) method. We show that the method performs well in comparison to the Generalized Steppingstone method on an empirical fixed-topology example from molecular phylogenetics involving 180 parameters. The LoRaD method can also be used to obtain the marginal likelihood in the variable-topology case if at least one tree topology occurs with sufficient frequency in the posterior sample to allow accurate estimation of the marginal likelihood conditional on that topology. [Bayesian; marginal likelihood; phylogenetics.]

 

In this work, we develop a novel Bayesian regression framework that can be used to complete variable selection in high dimensional settings. Unlike existing techniques, the proposed approach can leverage side information to inform about the sparsity structure of the regression coefficients. This is accomplished by replacing the usual inclusion probability in the spike and slab prior with a binary regression model which assimilates this extra source of information. To facilitate model fitting, a computationally efficient and easy to implement Markov chain Monte Carlo posterior sampling algorithm is developed via carefully chosen priors and data augmentation steps. The finite sample performance of our methodology is assessed through numerical simulations, and we further illustrate our approach by using it to identify genetic markers associated with the nicotine metabolite ratio; a key biological marker associated with nicotine dependence and smoking cessation treatment.

 

In many tissues, cell type varies over single-cell length-scales, creating detailed heterogeneities fundamental to physiological function. To gain understanding of the relationship between tissue function and detailed structure, and eventually to engineer structurally and physiologically accurate tissues, we need the ability to assemble 3D cellular structures having the level of detail found in living tissue. Here we introduce a method of 3D cell assembly having a level of precision finer than the single-cell scale. With this method we create detailed cellular patterns, demonstrating that cell type can be varied over the single-cell scale and showing function after their assembly. 

A new concentrated ternary salt ether‐based electrolyte enables stable cycling of lithium metal battery (LMB) cells with high‐mass‐loading (13.8 mg cm⁻², 2.5 mAh cm⁻²) NMC622 (LiNi_0.6Co_0.2Mn_0.2O₂) cathodes and 50 μm Li anodes. Termed “CETHER‐3,” this electrolyte is based on LiTFSI, LiDFOB, and LiBF₄with 5 vol% fluorinated ethylene carbonate in 1,2‐dimethoxyethane. Commercial carbonate and state‐of‐the‐art binary salt ether electrolytes were also tested as baselines. With CETHER‐3, the electrochemical performance of the full‐cell battery is among the most favorably reported in terms of high‐voltage cycling stability. For example, LiNi_xMn_yCo_1–x–yO₂(NMC)‐Li metal cells retain 80% capacity at 430 cycles with a 4.4 V cut‐off and 83% capacity at 100 cycles with a 4.5 V cut‐off (charge at C/5, discharge at C/2). According to simulation by density functional theory and molecular dynamics, this favorable performance is an outcome of enhanced coordination between Li⁺and the solvent/salt molecules. Combining advanced microscopy (high‐resolution transmission electron microscopy, scanning electron microscopy) and surface science (X‐ray photoelectron spectroscopy, time‐of‐fight secondary ion mass spectroscopy, Fourier‐transform infrared spectroscopy, Raman spectroscopy), it is demonstrated that a thinner and more stable cathode electrolyte interphase (CEI) and solid electrolyte interphase (SEI) are formed. The CEI is rich in lithium sulfide (Li₂SO₃), while the SEI is rich in Li₃N and LiF. During cycling, the CEI/SEI suppresses both the deleterious transformation of the cathode R‐3m layered near‐surface structure into disordered rock salt and the growth of lithium metal dendrites.

 

Autonomous Driving (AD) is a rapidly developing technology and its security issues have been studied by various recent research works. With the growing interest and investment in leveraging intelligent infrastructure support for practical AD, AD system may have new opportunities to defend against existing AD attacks. In this paper, we are the first to systematically explore such a new AD security design space leveraging emerging infrastructure-side support, which we call Infrastructure-Aided Autonomous Driving Defense (I-A2D2). We first taxonomize existing AD attacks based on infrastructure-side capabilities, and then analyze potential I-A2D2 design opportunities and requirements. We further discuss the potential design challenges for these I-A2D2 design directions to be effective in practice.